中国科学院行为科学重点实验室知识库

Prediction of daily mental wellbeing holds profound implications for individual healthcare and societal stability. Previous studies have shown the potential of using individual's multimodal behavioral data collected through mobile devices to predict his/her daily mental wellbeing metrics, such as stress, mood, and anxiety. However, effectively capturing long-range dependencies in behavioral time series data while accurately representing the statistical distribution patterns of various behaviors over a certain period is a significant challenge. In this paper, we propose a daily mental wellbeing prediction model based on a Topic-Guided Self-Attention Network (TGSAN). This model utilizes self-attention mechanism to capture long-range dependencies from the behavioral data collected by mobile devices. We utilize a multi-granularity time encoding method to inject time information of different granularities (i.e., day and hour, or week and day) into the behavioral data, thereby enhancing the sensibility of the self-attention network to capture every individual's habitual cyclicality rhythm. Then, we introduce a neural topic model to analyze the statistical distribution characteristics of various behaviors in the monitoring period as behavioral distribution patterns for different individuals, and further propose a topic attention network to enhance the model's classification performance by guiding the weights of long-range dependencies features from the self-attention network with the derived topic information. Compared to state-of-the-art methods, the proposed TGSAN achieved superior performance on datasets that measure different mental health indicators (stress, mood, and anxiety), with F1 scores outperforming by 4.5% and 2.3% on the Crosscheck and StudentLife datasets, respectively, and accuracy outperforming by 3.3% on the GLOBEM dataset. Our study demonstrates the effectiveness and interpretability of combining self-attention mechanisms with neural topic model, for a better understanding of the relationship between different individuals' behaviors and their mental wellbeing.